Data engineer, analytics engineer, data analyst, data architect, data scientist, machine learning engineer: Navigating the landscape of data engineering roles can be complicated, even for those immersed in the field. Data science encompasses a wide array of roles, and understanding the nuances between them can seem daunting. Today, we’ll focus on three key roles that are frequently confused: data engineer, analytics engineer and data analyst. Let’s dive in!
#1 The Data Engineer
As a data engineer, your day may begin with monitoring and maintaining data pipelines and databases. Your role will revolve around designing and implementing the infrastructure necessary for capturing data, processing and analysis. This demands critical problem-solving skills and creativity, with a strong focus on software development.
Key Skills:
A capable data engineer can:
- Write code and programming languages like Python, Java or Scala.
- Navigate database technologies like SQL.
- Work with structured and unstructured data.
- Develop machine-learning endpoints.
#2 The Analytics Engineer
Analytics engineering serves as a bridge between data engineering and data analysis, leveraging the platforms and processes created by the data engineer. The role of analytics engineer emerged in the 2010s in response to the evolving data landscape, along with the rise of data build tool (dbt) and cloud-based data warehouses. As a professional in this role, you’ll possess a blend of software expertise and business acumen and be responsible for ensuring clean datasets, applying best software practices and approaching data challenges from both a technical and business perspective.
Key Skills:
Skilled analytics engineers are:
- Less focused on writing code; more focused on using SQL queries and data modeling techniques.
- Experts in data warehouse and data visualization tools.
- Trained in proper documentation and consolidation of data sources.
- Able to teach business users to use software.
#3 The Data Analyst
As a data analyst, you’ll play a vital role in extracting actionable insights from data sets prepared by the data engineer and analytics engineer. While not exclusive to the engineering sector, data analysis focuses on drilling down into data sets, identifying trends and contributing to business decisions. You’ll often also focus on the business side of key issues.
Key Skills:
A successful data analyst will:
- Be proficient in non-code-heavy data visualization tools like Tableau, Power BI or Excel.
- Identify trends and patterns through statistical analysis and data mining.
- Contribute to business decisions based on deep insights and forecasts from data sets.
How Do the Salaries Compare?
While actual amounts may vary, the estimated annual salaries for these roles are:
• Data Engineer: $131,000 (median pay across all years of experience)
• Analytics Engineer: $189,000 (median pay across all years of experience)
• Data Analyst: $108,000 (median pay across all years of experience)
Which Role Is Right For You?
Are you drawn to big data and coding? Are you intrigued by building data infrastructure but prefer less analysis? If so, consider a path as a data engineer, focusing on system-level solutions. If you prefer starting your day exploring freshly prepared data and preparing forecasts, then you’ll probably want to consider becoming a data analyst. If you enjoy complex logic, data cleaning and software development, you might thrive as an analytics engineer.
Keep in mind that some companies value professionals who can navigate hybrid roles, combining various data disciplines. Ultimately, where you fit on the data spectrum depends on the tools and skills you prefer to use.
Master Big Data With a Master’s Degree in Data Analytics
If your passion lies in big data engineering, you can’t go wrong with an online Master of Engineering: Engineering with a Data Analytics planned option from the University of Wisconsin–Madison. You’ll become well prepared to embark on a successful career as a data engineer, analytics engineer, analyst or another role at the intersection of data and engineering. Some of the diverse skills you’ll learn are:
- coding proficiency
- machine learning development
- data visualization techniques
- business analytics
Take the first step toward unlocking your potential in the dynamic field of engineering data analytics. Apply now and embark on a journey of innovation and discovery.
Sources:
https://www.getcensus.com/blog/analytics-engineer-or-data-engineer-whos-right-for-the-job
https://www.linkedin.com/pulse/day-life-data-analyst-careeristlearning
https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm
https://www.glassdoor.com/Salaries/analytics-engineer-salary-SRCH_KO0,18.htm
https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm